Yeast alleles involved in tolerance to high alcohol levels

ABSTRACT

The present application relates to the field of yeast and, specifically, to the identification of yeast alleles that are involved in maximal alcohol accumulation and/or in tolerance to high alcohol levels. Preferably, the alcohol is ethanol. The identified alleles can be combined or stacked with each other to construe and/or select high alcohol tolerant yeasts, most notably  Saccharomyces  species.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national phase entry under 35 U.S.C. § 371 ofInternational Patent Application PCT/EP2016/070732, filed Sep. 2, 2016,designating the United States of America and published in English asInternational Patent Publication WO 2017/037241 A1 on Mar. 9, 2017,which claims the benefit under Article 8 of the Patent CooperationTreaty to European Patent Application Serial No. 15183670.7, filed Sep.3, 2015.

TECHNICAL FIELD

The present application relates to the field of yeast and, specifically,to the identification of yeast alleles that are involved in maximalalcohol accumulation and/or intolerance to high alcohol levels.Preferably, the alcohol is ethanol. The identified alleles can becombined or stacked with each other to construe and/or select highalcohol-tolerant yeasts, most notably Saccharomyces species.

STATEMENT ACCORDING TO 37 C.F.R. § 1.821(c) or (e)—SEQUENCE LISTINGSUBMITTED AS ASCII TEXT FILE

Pursuant to 37 C.F.R. § 1.821(c) or (e), a file containing an ASCII textversion of the Sequence Listing has been submitted concomitant with thisapplication, titled V525_ST25.txt, created on Feb. 22, 2018 and having asize of 18034 bytes, the contents of which are hereby incorporated byreference.

BACKGROUND

The capacity to produce high levels of alcohol is a very rarecharacteristic in nature. It is most prominent in the yeastSaccharomyces cerevisiae, which is able to accumulate in the absence ofcell proliferation, ethanol concentrations in the medium of more than17% (V/V), a level that kills virtually all competing microorganisms. Asa result, this property allows this yeast to outcompete all othermicroorganisms in environments rich enough in sugar to sustain theproduction of such high ethanol levels (Casey and Ingledew, 1986;D'Amore and Stewart, 1987). Very few other microorganisms, e.g., theyeast Dekkera bruxellensis, have independently evolved a similar butless pronounced ethanol tolerance compared to S. cerevisiae (Rozpedowskaet al., 2011). The capacity to accumulate high ethanol levels lies atthe basis of the production of nearly all alcoholic beverages as well asbioethanol in industrial fermentations by the yeast S. cerevisiae.Originally, all alcoholic beverages were produced with spontaneousfermentations in which S. cerevisiae gradually increases in abundance,in parallel with the increase in the ethanol level, to finally dominatethe fermentation at the end. The ability to survive and proliferate inhigh levels of ethanol is an ecologically important and industriallyrelevant trait of yeast cells. The ethanol produced by yeast cells slowsdown growth of competing microbes, but at higher concentrations, itcauses stress for the yeast cells themselves. Different yeast strainsshow significant differences in their ability to grow in the presence ofethanol, with the more ethanol-tolerant ones likely having a fitnessadvantage over non-tolerant strains.^([1-3]) Moreover, ethanol toleranceis a key trait of industrial yeasts that often encounter very highethanol concentrations, for example, during beer and wine making andindustrial bio-ethanol production.

The genetic basis of yeast alcohol tolerance, particularly ethanoltolerance, has attracted much attention but, until recently, nearly allresearch was performed with laboratory yeast strains, which display muchlower alcohol tolerance than the natural and industrial yeast strains.This research has pointed to properties like membrane lipid composition,chaperone protein expression and trehalose content, as majorrequirements for ethanol tolerance of laboratory strains (D'Amore andStewart, 1987; Ding et al., 2009), but the role played by these factorsin other genetic backgrounds and in establishing tolerance to very highethanol levels has remained unknown. Different experimental approacheshave been used, including screening of (deletion) mutants for increasedethanol tolerance, transcriptome analysis of ethanol-stressed cells andQTL analyses aimed at identifying mutations that cause differences inethanol tolerance between different yeast strains.^([4-10]) A polygenicanalysis of the high ethanol tolerance of a Brazilian bioethanolproduction strain VR1 revealed the involvement of several genespreviously never connected to ethanol tolerance and did not identifygenes affecting properties classically considered to be required forethanol tolerance in lab strains (Swinnen et al., 2012a). Together,these studies have linked multiple different genetic loci to ethanoltolerance and identified hundreds of genes involved in a multitude ofcellular processes.^([11-14])

A shortcoming of most previous studies is the assessment of alcoholtolerance solely by measuring growth on nutrient plates in the presenceof increasing alcohol levels (D'Amore and Stewart, 1987; Ding et al.,2009). This is a convenient assay, which allows hundreds of strains orsegregants to be phenotyped simultaneously with little work andmanpower. However, a more physiologically and ecologically relevantparameter of alcohol tolerance in S. cerevisiae is its capacity toaccumulate by fermentation high alcohol levels in the absence of cellproliferation. This generally happens in an environment with a largeexcess of sugar compared to other essential nutrients. As a result, alarge part of the alcohol in a typical, natural or industrial, yeastfermentation is produced with stationary phase cells in the absence ofany cell proliferation. The alcohol tolerance of the yeast under suchconditions determines its maximal alcohol accumulation capacity, aspecific property of high ecological and industrial importance. Inindustrial fermentations, a higher maximal alcohol accumulation capacityallows a better attenuation of the residual sugar and, therefore,results in a higher yield. A higher final alcohol titer reduces thedistillation costs and also lowers the liquid volumes in the factory,which has multiple beneficial effects on costs of heating, cooling,pumping and transport of liquid residue. It also lowers microbialcontamination and the higher alcohol tolerance of the yeast generallyalso enhances the rate of fermentation, especially in the later stagesof the fermentation process. Maximal alcohol accumulation capacity canonly be determined in individual yeast fermentations, which are muchmore laborious to perform than growth tests on plates. In staticindustrial fermentations, maintenance of the yeast in suspension is dueto the strong CO₂ bubbling and this can only be mimicked in lab scalewith a sufficient amount of cells in a sufficiently large volume.

While it becomes increasingly clear that ethanol is a complex stressthat acts on several different processes including increasing fluidityand permeability of cellular membranes, changing activity and solubilityof membrane-bound and cytosolic proteins and interfering with the protonmotive force (for review, see references 15-17 herein, the exactmolecular mechanisms and genetic architecture underlying ethanoltolerance are still largely unknown.

Experimental evolution to study adaptation to increased ethanol levelscould provide more insight into the molecular mechanisms underlyingethanol tolerance since such experiments could reveal differentmutational paths that make a sensitive strain more tolerant. Only ahandful of studies have looked at adaptation to ethanol in originallynon-ethanol-tolerant microbes exposed to gradually increasing levels ofethanol.^([6, 18-22]) These have mostly focused on the physiologicaladaptations found in the evolved cells and have not performed anextensive analysis of the mechanisms and genetic changes underlying thisadaptation. Hence, a comprehensive analysis of the type and number ofmutations a non-ethanol-tolerant strain can (or needs to) acquire tobecome more ethanol tolerant is still lacking. Experimental evolutionhas proven to be a valuable tool to investigate the different mechanismsand pathways important for cells to adapt to specific selectiveconditions. Seminal papers have increased the understanding of themolecular basis of adaptation to specific stresses, such as heat stress,nutrient limitation and antibiotic treatment.^([32-28]) Recent advancesin DNA sequencing technologies allow affordable and fast sequencing ofcomplete genomes of clones and populations. While sequencing clonesyields information on individual lineages within the experiment,population data provides information on the heterogeneity of adaptation.Additionally, sequencing samples isolated at different time pointsduring the evolution experiment makes it possible to capture evolutionin action. This has provided valuable information on the rate and typesof mutations underlying adaptation, the genetic basis of “novel”phenotypes and the existence of parallel pathways to establishcomparable phenotypic outcomes.^([29-32]) For example, a common strategyobserved in populations evolving under nutrient limitation is theamplification of genetic regions encoding transporters responsible forthe uptake of the limiting nutrient.^([24-33]) Other studies usingmultiple replicate populations have discovered a high degree ofparallelism in the adaptive solutions found by different populations.Clonal interference, the competition between lineages carrying differentbeneficial mutations, is another commonly observed phenomenon inevolution of asexually propagating populations that can increasecomplexity of mutational dynamics as well as impede the spread ofbeneficial mutations in a population.^([32, 34, 35]) To unravel themolecular mechanisms of adaptation to a specific condition, most studieshave used isogenic replicate populations, with all cells having the sameinitial genome size. Genome size can significantly change duringevolution; with both small-scale changes (chromosomal 108 deletions andamplifications) and large-scale changes (increase or decrease inploidy). Moreover, ploidy shifts have been reported in the evolutionaryhistory of many organisms, including Saccharomyces cerevisiae and as aresponse to selective pressure.^([36-38]) Conversely, genome size hasalso been reported to affect evolution rate: polyploidy has been shownto increase adaptability.^([39, 40]) Multiplying the amount of DNAincreases the genetic material available for evolution to tinker withand can alter gene expression.^([41-43]) These polyploid genomes can beunstable, resulting in loss of chromosomes and, thus, aneuploidcells.^([44-46]) Although studies have looked at adaptation of lineagesof different ploidy, none have followed in detail the mutationaldynamics in these evolving populations over time.^([36, 47-49])

It would be advantageous to identify novel pathways and alleles thatcontribute to alcohol tolerance and are relevant in industrial yeaststrains as well, to further increase yield of alcohol fermentations withsuch strains. Indeed, even a slight increase in alcohol tolerance oralcohol accumulation capacity would have huge economic benefits.

BRIEF SUMMARY

Experimental evolution of isogenic yeast populations of differentinitial ploidy was used to study adaptation to increasing levels ofethanol. Evolved lineages showed a significant increase in ethanoltolerance compared to ancestral strains. High coverage whole-genomesequencing of more than 30 evolved populations and over 100 adaptedclones isolated throughout a two-year evolution experiment revealed howa complex interplay of different evolutionary mechanisms led to highertolerance, including de novo single nucleotide mutations, extensive copynumber variation and ploidy changes. Although the specific mutationsdiffer between different evolved lineages, application of a novelcomputational pipeline to identify target pathways reveals shared themesat the level of functional modules. Moreover, by combining an allelicreplacement approach with high-throughput fitness measurements, severalSNPs that arose in the adapted cells previously not implicated inethanol tolerance were identified and that significantly increasedethanol tolerance when introduced into a non-tolerant background. Takentogether, the results show how, in contrast to adaptation to some otherstresses, adaptation to a complex and severe stress involves aninterplay of different evolutionary mechanisms. In addition, the studyhighlights the potential of experimental evolution to identify mutationsthat are of industrial importance.

Provided are alleles that increase alcohol tolerance and/or alcoholaccumulation in yeast. Most particularly, the alcohol is ethanol. Mostparticularly, the yeast is a Saccharomyces species. Increase in alcoholtolerance is an economically relevant property and, as a non-limitingexample, an increase in alcohol tolerance and/or accumulation may befavorable for bio-ethanol production. The increase in alcohol toleranceand/or accumulation may result in a higher speed of fermentation (i.e.,less time needed to reach a particular percentage of alcohol) and/or ahigher final ethanol titer.

According to specific embodiments, such alleles are selected from anMEX67 allele, a PCA1 allele, a PRT1 allele, a YBL059W allele, an HEM13allele, an HST4 allele, and a VPS70 allele. The alleles can also be inintergenic regions. According to these embodiments, the alleles aretypically selected from the intergenic region of Chromosome IV(particularly around or at position 1489310) and the intergenic regionof Chromosome XII (particularly around or at position 747403).

Particularly envisaged alleles are selected from the group consisting ofan MEX67 allele with a G456A mutation, PCA1 with a C1583T mutation, PRT1with an A1384G mutation, YBL059W with a G479T mutation, HEM13 with aG700C mutation, HST4 with a G262C mutation, VPS70 with a C595A mutation,the intergenic region of Chromosome W with an A>T substitution atposition 1489310, and the intergenic region of Chromosome XII with aC>Tsubstitution at position 747403. It is particularly envisaged that thewild-type alleles mentioned are selected from MEX67 as shown in SEQ IDNO: 1, PCA1 as shown in SEQ ID NO: 2, PRT1 as shown in SEQ ID NO: 3,YBL059W as shown in SEQ ID NO: 4, HEM13 as shown in SEQ ID NO: 5, HST4as shown in SEQ ID NO: 6, and VPS70 as shown in SEQ ID NO: 7. Thus, itis particularly envisaged that the allele is selected from the groupconsisting of SEQ ID NO: 1 with a G456A mutation, SEQ ID NO: 2 with aC1583T mutation, SEQ ID NO: 3 with an A1384G mutation, SEQ ID NO: 4 witha G479T mutation, SEQ ID NO: 5 with a G700C mutation, SEQ ID NO: 6 witha G262C mutation, or SEQ ID NO: 7 with a C595A mutation.

The alleles can be used to increase alcohol tolerance and/or alcoholaccumulation in yeast, either alone or in combination. The latterprovides additive or even synergistic effects. Anypermutation/combination of the nine alleles can be used to increasealcohol tolerance and/or alcohol accumulation, and this is explicitlyenvisaged herein. Any combination of these alleles (including singlealleles) may also be combined with known mutations or alleles thatincrease alcohol tolerance and/or alcohol accumulation. This isparticularly the case when the alleles are to be used in industrialyeast strains adapted to have high alcohol tolerance.

Particularly envisaged combinations are those with the MEX67 allele.According to these embodiments, an MEX67 allele is provided to increasealcohol tolerance and/or alcohol accumulation in yeast. This MEX67allele may be combined with other alcohol tolerance and/oraccumulation-modulating alleles. This may be known as alcohol tolerancealleles. It is also explicitly foreseen that the MEX67 allele may beincorporated in an industrial yeast strain adapted to have high alcoholtolerance (and that thus already has a combination of known alcoholtolerance alleles). However, it is also explicitly envisaged that theMEX67 allele is further combined with the new alcohol tolerance and/oraccumulation-modulating alleles reported herein. According to theseembodiments, the use of a MEX67 allele is provided, wherein the MEX67allele is combined with other alcohol tolerance and/oraccumulation-modulating alleles. Particularly, the other alcoholtolerance and/or accumulation-modulating alleles are selected from thegroup consisting of PCA1, PRT1, YBL059W, HEM13, HST4, and VPS70.According to alternative embodiments, they may also be selected from theintergenic region of Chromosome IV (particularly around or at position1489310) and the intergenic region of Chromosome XII (particularlyaround or at position 747403). According to specific embodiments, theMEX67 allele has a G456A mutation. According to further specificembodiments, the other alcohol tolerance and/or accumulation-modulatingalleles are selected from the group consisting of PCA1 with a C1583Tmutation, PRT1 with an A1384G mutation, YBL059W with a G479T mutation,HEM13 with a G700C mutation, HST4 with a G262C mutation, VPS70 with aC595A mutation, the intergenic region of Chromosome IV with an A>Tsubstitution at position 1489310, and the intergenic region ofChromosome XII with a C>T substitution at position 747403.

Albeit that the combinations with the MEX67 alleles are particularlyenvisaged, the above applies mutatis mutandis to the other allelesdescribed herein (e.g., the PCA1, PRT1, YBL059W, . . . alleles).

The alleles described herein have not before been linked to increasedalcohol tolerance or accumulation. This is also true for the genes andintergenic regions, with exception of VPS70. For VPS70, another allelehas recently been shown to also modulate alcohol tolerance(WO2014/170330). Thus, according to particular embodiments, VPS70 isexcluded from the envisaged combinations. According to alternativeembodiments, when a VPS70 allele is used to increase alcohol toleranceand/or accumulation, it is the VPS70 allele with a C595A mutation.

According to a further aspect, the alleles provided herein, particularlythe MEX67 allele, are used for selecting a yeast strain with higheralcohol accumulation and/or resistance. Particularly, the yeast is aSaccharomyces spp. Particularly, the alcohol is ethanol.

The selection of the strain can be carried out with every method knownto the person skilled in the art. As a non-limiting example, strains maybe selected on the basis of an identification of the allele by PCR orhybridization. The selection may be combined by a selection for otheralleles, known to be involved in alcohol accumulation and/or alcoholtolerance, such as, but not limited to, specific alleles of ADE1, VPS70,MKT1, APJ1, SWS2, or KIN3. The selection may be carried outsimultaneously or consecutively. In the case of a consecutive selection,the sequence of the selection is not important, i.e., the selectionusing MEX67 may be carried out before or after the other selectionrounds.

BRIEF DESCRIPTION OF THE FIGURES

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1: Experimental setup. (Panel A) Experimental evolution ofprototrophic, isogenic populations of different ploidy (haploid (VK111),diploid (VK145) and tetraploid (VK202)) for increased ethanol tolerancewas performed in a turbidostat. Every 25 generations, the ethanolconcentration in the media was increased in a stepwise manner (startingat 6% (v/v) and reaching 12% at 200 generations). Increasing the ethanolconcentration from 10% to 11% dramatically reduced growth rate ofevolving cells. Therefore, instead of increasing ethanol levels, theethanol level was first reduced to 10.7% after 100 generations. (PanelB) Red circles represent sampling points (indicated as number ofgenerations) for which whole-genome sequencing was performed. For eachcircle, heterogeneous populations as well as three evolved,ethanol-tolerant clones were sequenced. Sequencing of the populationsample of reactor 4 at 200 generations failed, so this data is omittedfrom the specification. For generation 80 of reactor 1, only populationdata is available.

FIG. 2: Evolved populations are more ethanol tolerant. Evolvedpopulations from different reactors show increased fitness in EtOH.Fitness was determined for population samples of each reactor after 40generations (blue) and 200 generations (red). Fitness is expressedrelative to the ancestral strain of each reactor (haploid for reactor1-2; diploid for reactor 3-4; and tetraploid for reactor 5-6). Datarepresent the average of three independent measurements; error barsrepresent standard deviation.

FIG. 3: Haploid lineages diploidized during adaptation to EtOH. Flowcytometry analysis of DNA content (stained by propidium iodide) ofevolved populations, compared to ancestral haploid (red) and diploid(blue). For the clonal ancestral samples, two peaks are observed,corresponding to the G1 and G2 phase of the cell cycle. Evolvedpopulations sometimes display three peaks, indicative of both haploidand diploid subpopulations.

FIG. 4: Mutations present in evolved clones isolated from all reactorsat 200 generations. Circle diagrams depict the number and types ofmutations identified in evolved clones at 200 generations of reactor 1,reactor 2, reactor 3, reactor 4, reactor 5 and reactor 6.

FIG. 5: Copy number variation in evolved clones of reactors 2 and 3isolated at 200 generations. Genome view of yeast chromosomes and CNVpatterns from a sliding window analysis. The y axis represents log 2ratios of the coverage observed across 500 bp genomic windows to thecoverage expected in a diploid genome without CNVs. Area of the plotlocated between the red lines (from −0.23 to −1) marks putative CNV lossevents, whereas the region between the blue lines 270 (from 0.23 to0.58) marks putative CNV gain events. It should be noted that theamplified region of chromosome XII observed in some of the clones doesnot correspond to the ribosomal DNA genes.

FIG. 6: Dynamics and linkage of mutations in evolved populations ofreactor 2. Mutations (reaching a frequency of least 20% in the evolvedpopulation samples) and corresponding frequencies were identified frompopulation sequencing data. Muller diagram represents the hierarchicalclustering of these mutations, with each color block representing aspecific group of linked mutations. Indels are designated with I,whereas heterozygous mutations are in italics. Mutations present asheterozygous mutations in all clones of a specific time point andpresent at a frequency of 50% in the population, are depicted as afrequency of 100% in the population, since it is expected that all cellsin the population contain this mutation. After 80 generations, a mutatorphenotype appeared in this reactor (indicated by arrow under graph),which coincides with the rise in frequency of an indel in the mismatchrepair gene MSH2. Frequencies of haplotypes, dynamics and linkage forreactor 1 were calculated but are not shown.

FIG. 7: Adaptive pathways are involved in cell cycle, DNA repair andprotoporphyrinogen metabolism. Shown is the sub-network that prioritizesputative adaptive mutations by applying PheNetic on all selectedmutations, excluding those originating from the populations with amutator phenotype, i.e., reactors 2 and 6. The nodes in the networkcorrespond to genes and/or their associated gene products. Node bordersare colored according to the reactors containing the populations inwhich these genes were mutated. Nodes are colored according to genefunction, for each gene the most enriched term is visualized (greyindicates no enrichment). Cell cycle-related processes have beensubdivided into DNA replication and interphase. The edge colors indicatedifferent interaction types. Orange lines represent metabolicinteractions, green lines represent protein-protein interactions, andred lines represent protein-DNA interactions. Sub-networks wereextracted by separately analyzing the mutated genes observed in each ofthe different populations (reactors)—this data is not shown here.

FIG. 8: Single mutations present in evolved populations can increaseethanol tolerance of a non-adapted strain. Plots show the averageselection coefficient (smut) as a function of ethanol concentration for(Panel A) pca1^(C1583T), (Panel B) prt1^(A1384G), (Panel C)ybl059w^(G479T), (Panel D) intergenic ChrIV A1489310T, (Panel E)hem13^(G700C), (Panel F) intergenic ChrXII C747403T, (Panel G)hst4^(G262C), (Panel H) vps70^(C595A), and (Panel I) mex67^(G456A).Superscripts denote the exact nucleotide change in each of the mutantstested. YECitrine-tagged mutants were competed with the mCherry-taggedparental strain (orange dots); dye-swap experiments were carried out bycompeting the mCherry-tagged mutants with the YECitrine parental strain(blue dots), except for (Panel I). Error bars show the S.E.M. from threeexperimental replicates. Asterisk shows P-values from the one-way ANOVAtests of the mean differences in 4-8% ethanol compared to fitness in 0%ethanol: * p<0.05; ** p<0.01; ***p<0.005.

FIG. 9: Mutations identified in lab strains also increase alcoholtolerance in an industrial reference strain. Coding mutations wereintroduced into the industrial reference biofuel strain Ethanol Red.Ethanol production of these mutants is shown, relative to its propercontrol. All experiments were repeated four times (real biologicalreplicates, using independent transformants where available).

FIG. 10: Fermentation performance of mutant strains is comparable towild-type strain. Coding mutations were introduced into the industrialreference biofuel strain Ethanol Red. Fermentation performance in YP+35%glucose (w/v) is shown as the cumulative weight loss—a proxy for CO₂production—during the fermentation. Each line represents the average offour replicate fermentations, error bars represent standard deviation.

DETAILED DESCRIPTION Definitions

This disclosure will be described with respect to particular embodimentsand with reference to certain drawings but the disclosure is not limitedthereto but only by the claims. Any reference signs in the claims shallnot be construed as limiting the scope. The drawings described are onlyschematic and are non-limiting. In the drawings, the size of some of theelements may be exaggerated and not drawn on scale for illustrativepurposes. Where the term “comprising” is used in the present descriptionand claims, it does not exclude other elements or steps. Where anindefinite or definite article is used when referring to a singularnoun, e.g., “a,” “an,” or “the,” this includes a plural of that noununless something else is specifically stated.

Furthermore, the terms “first,” “second,” “third,” and the like in thedescription and in the claims, are used for distinguishing betweensimilar elements and not necessarily for describing a sequential orchronological order. It is to be understood that the terms so used areinterchangeable under appropriate circumstances and that the embodimentsof the disclosure described herein are capable of operation in othersequences than described or illustrated herein.

The following terms or definitions are provided solely to aid in theunderstanding of the disclosure. Unless specifically defined herein, allterms used herein have the same meaning as they would to one skilled inthe art of the present disclosure. Practitioners are particularlydirected to Sambrook et al., Molecular Cloning: A Laboratory Manual,2^(nd) ed., Cold Spring Harbor Press, Plainsview, N.Y. (1989); andAusubel et al., Current Protocols in Molecular Biology (Supplement 47),John Wiley & Sons, New York (1999), for definitions and terms of theart. The definitions provided herein should not be construed to have ascope less than understood by a person of ordinary skill in the art.

An “allele,” as used herein, is a specific form of the gene that iscarrying SNPs or other mutations, either in the coding (reading frame)or the non-coding (promoter region, or 5′ or 3′ non-translated end) partof the gene, wherein the mutations distinguish the specific form fromother forms of the gene.

“Gene,” as used herein, includes both the promoter and terminator regionof the gene as well as the coding sequence. It refers both to thegenomic sequence (including possible introns) as well as to the cDNAderived from the spliced messenger, operably linked to a promotersequence.

“Coding sequence” is a nucleotide sequence that is transcribed into mRNAand/or translated into a polypeptide when placed under the control ofappropriate regulatory sequences. The boundaries of the coding sequenceare determined by a translation start codon at the 5′-terminus and atranslation stop codon at the 3′-terminus. A coding sequence caninclude, but is not limited to, mRNA, cDNA, recombinant nucleotidesequences or genomic DNA, while introns may be present as well undercertain circumstances.

“Promoter region” of a gene as used herein refers to a functional DNAsequence unit that, when operably linked to a coding sequence andpossibly a terminator sequence, as well as possibly placed in theappropriate inducing conditions, is sufficient to promote transcriptionof the coding sequence.

“Nucleotide sequence,” “DNA sequence,” or “nucleic acid molecule(s)” asused herein refers to a polymeric form of nucleotides of any length,either ribonucleotides or deoxyribonucleotides. This term refers only tothe primary structure of the molecule. Thus, this term includes double-and single-stranded DNA and RNA. It also includes known types ofmodifications, for example, methylation, “caps” substitution of one ormore of the naturally occurring nucleotides with an analog.

“Modulation of alcohol accumulation and/or tolerance,” as used herein,means an increase or a decrease of the alcohol concentration, producedby the yeast carrying the specific allele, as compared with the alcoholconcentration produced under identical conditions by a yeast that isgenetically identical, apart from the specific allele(s).

“Alcohol,” as used herein, can be any kind of alcohol including, but notlimited to, methanol, ethanol, n- and isopropanol, and n- andisobutanol. Indeed, several publications indicate that the tolerance toethanol and other alkanols is determined by the same mechanisms (Carlsenet al., 1991; Casal et al., 1998).

The causes and mechanisms underlying evolutionary adaptation are keyissues in biology. However, the mechanisms underlying evolutionaryadaptation to complex and severe stress factors (e.g., broadly actingtoxins or extreme niches that require immediate, complex adaptivechanges) remain understudied. Here, how yeast populations adapt togradually increasing ethanol levels is studied—a broad-acting,ecologically and industrially relevant complex stress that is stillpoorly understood. The results reveal how over a two-year evolutionperiod, several evolutionary mechanisms, including mutator phenotypes,changes in ploidy, and complex clonal interference, result in adaptedpopulations capable of surviving in medium containing up to 12% ethanol.In addition, several previously unknown adaptive mutations that increaseethanol resistance are reported, which may open new routes to increasethe efficiency of industrial fermentations. Specifically, six initiallyisogenic yeast populations of different ploidy were exposed toincreasing levels of ethanol. High-coverage, whole-genome sequencing ofmore than 30 populations and 100 clones isolated throughout thistwo-year evolution experiment were combined with a novel computationalpipeline to reveal the mutational dynamics, molecular mechanisms andnetworks underlying increased ethanol tolerance of the evolved lineages.High-throughput fitness measurements allowed characterization of thephenotypic effect of identified mutations in different environments. Theresults suggest that adaptation to high ethanol is complex and can bereached through different mutational pathways. It was found thatadaptation to high ethanol levels involves the appearance of mutatorphenotypes and evolving populations showing strong clonal interference.Evolved cells display extensive variation in genome size, with initiallyhaploid and tetraploid populations showing quick convergence to adiploid state. The results are the first to attribute a significantfitness advantage of a diploid cell over an isogenic haploid cell underselective conditions. In addition, evolved clones repeatedly gainedextra copies of the same chromosomes. By combining an allelicreplacement approach with high-throughput fitness measurements, severalmutations previously not implicated in ethanol tolerance were identifiedthat significantly increased ethanol tolerance when introduced into anon-tolerant background. Together, the study yields a detailed view ofthe molecular evolutionary processes and genetic changes underlyinglong-term adaptation to a severe and complex stress, and highlights thepotential of experimental evolution to identify mutations that are ofindustrial importance. The methods developed and insights gained serveas a model for adaptation to a complex and lethal stress. Moreover,specific adaptive SNPs can be used to guide engineering strategies aimedat obtaining superior biofuel yeasts. Last but not least, theexperimental setup is also interesting because it takes an atypicalapproach. In many experimental evolution studies, populations areexposed for a limited amount of time (typically a few months) to adefined, constant and “simple” stress (e.g., a fixed concentration ofantibiotic or limiting nutrient), so that in the beginning of theexperiment, cells are confronted with a very strong selection, while theselection pressure is reduced or even eliminated when cells becomeresistant. In the experimental set-up, ethanol levels were graduallyincreased over time so that cells were evolving under constant selectionover a long (two-year) period. High concentrations of ethanol slow downgrowth and actively kill non-tolerant cells. The work disclosed hereinthus combines aspects of traditional evolution studies with principlesused in so-called morbidostat experiments and may, therefore, offer arealistic picture of adaptation to a complex and severe stress where apopulation gradually penetrates a new niche.

Provided are alleles that increase alcohol tolerance and/or alcoholaccumulation in yeast. Most particularly, the alcohol is ethanol. Mostparticularly, the yeast is a Saccharomyces species.

According to specific embodiments, such alleles are selected from anMEX67 allele, a PCA1 allele, a PRT1 allele, a YBL059W allele, an HEM13allele, an HST4 allele, and a VPS70 allele. The alleles can also be inintergenic regions. According to these embodiments, the alleles aretypically selected from the intergenic region of Chromosome IV(particularly around or at position 1489310) and the intergenic regionof Chromosome XII (particularly around or at position 747403).

Particularly envisaged alleles are selected from the group consisting ofan MEX67 allele with a G456A mutation, PCA1 with a C1583T mutation, PRT1with an A1384G mutation, YBL059W with a G479T mutation, HEM13 with aG700C mutation, HST4 with a G262C mutation, VPS70 with a C595A mutation,the intergenic region of Chromosome IV with an A>T substitution atposition 1489310, and the intergenic region of Chromosome XII with aC>Tsubstitution at position 747403.

The alleles can be used to increase alcohol tolerance and/or alcoholaccumulation in yeast, either alone or in combination. The latterprovides additive or even synergistic effects. Anypermutation/combination of the nine alleles can be used to increasealcohol tolerance and/or alcohol accumulation, and this is explicitlyenvisaged herein. Any combination of these alleles (including singlealleles) may also be combined with known mutations or alleles thatincrease alcohol tolerance and/or alcohol accumulation. This isparticularly the case when the alleles are to be used in industrialyeast strains adapted to have high alcohol tolerance.

Particularly envisaged combinations are those with the MEX67 allele.According to these embodiments, the MEX67 allele is provided to increasealcohol tolerance and/or alcohol accumulation in yeast. This MEX67allele may be combined with other alcohol tolerance and/oraccumulation-modulating alleles. This may be known alcohol tolerancealleles. It is also explicitly foreseen that the MEX67 allele may beincorporated in an industrial yeast strain adapted to have high alcoholtolerance (and that thus already has a combination of known alcoholtolerance alleles). Non-limiting examples include ADE1, INO1, VPS70,MKT1, APJ1, SWS2, PDR1, or KIN3.

However, it is also explicitly envisaged that the MEX67 allele isfurther combined with the new alcohol tolerance and/oraccumulation-modulating alleles reported herein. According to theseembodiments, the use of an MEX67 allele is provided, wherein the MEX67allele is combined with other alcohol tolerance and/oraccumulation-modulating alleles. Particularly, the other alcoholtolerance and/or accumulation-modulating alleles are selected from thegroup consisting of PCA1, PRT1, YBL059W, HEM13, HST4, and VPS70.According to alternative embodiments, they may also be selected from theintergenic region of Chromosome IV (particularly around or at position1489310) and the intergenic region of Chromosome XII (particularlyaround or at position 747403). According to specific embodiments, theMEX67 allele has a G456A mutation. According to further specificembodiments, the other alcohol tolerance and/or accumulation-modulatingalleles are selected from the group consisting of PCA1 with a C1583Tmutation, PRT1 with an A1384G mutation, YBL059W with a G479T mutation,HEM13 with a G700C mutation, HST4 with a G262C mutation, VPS70 with aC595A mutation, the intergenic region of Chromosome IV with an A>Tsubstitution at position 1489310, and the intergenic region ofChromosome XII with a C>T substitution at position 747403.

Albeit that the combinations with the MEX67 alleles are particularlyenvisaged, the above applies mutatis mutandis to the other allelesdescribed herein (e.g., the PCA1, PRT1, YBL059W, . . . alleles).

The alleles described herein have not before been linked to increasedalcohol tolerance or accumulation. This is also true for the genes andintergenic regions, with exception of VPS70. For VPS70, another allelehas recently been shown to also modulate alcohol tolerance. Thus,according to particular embodiments, VPS70 is excluded from theenvisaged combinations. According to alternative embodiments, when aVPS70 allele is used to increase alcohol tolerance and/or accumulation,it is the VPS70 allele with a C595A mutation.

According to a further aspect, the alleles provided herein, particularlythe MEX67 allele, are used for selecting a yeast strain with higheralcohol accumulation and/or resistance. Particularly, the yeast is aSaccharomyces spp. Particularly, the alcohol is ethanol.

In a specific embodiment, the disclosure provides a yeast strain,particularly an industrial yeast strain, particularly an industrialyeast strain of Saccharomyces cerevisiae comprising at least one alleleselected from the group consisting of an MEX67 allele with a G456Amutation, PCA1 with a C1583T mutation, PRT1 with an A1384G mutation,YBL059W with a G479T mutation, HEM13 with a G700C mutation, HST4 with aG262C mutation, VPS70 with a C595A mutation, the intergenic region ofChromosome IV with an A>T substitution at position 1489310, and theintergenic region of Chromosome XII with a C>T substitution at position747403.

The selection of the strain can be carried out with every method knownto the person skilled in the art. As a non-limiting example, strains maybe selected on the basis of an identification of the allele by PCR orhybridization. The selection may be combined by a selection for otheralleles, known to be involved in alcohol accumulation and/or alcoholtolerance, such as, but not limited to, specific alleles of ADE1, VPS70,MKT1, APJ1, SWS2, or KIN3. The selection may be carried outsimultaneously or consecutively. In the case of a consecutive selection,the sequence of the selection is not important, i.e., the selectionusing MEX67 may be carried out before or after the other selectionrounds.

It is to be understood that although particular embodiments, specificconfigurations as well as materials and/or molecules, have beendiscussed herein for cells and methods according to this disclosure,various changes or modifications in form and detail may be made withoutdeparting from the scope and spirit of this disclosure. The followingExamples are provided to better illustrate particular embodiments, andthey should not be considered limiting the application. The applicationis limited only by the claims.

EXAMPLES Example 1. Experimental Evolution to Increase Alcohol Tolerance

Introduction

In this study, experimental evolution is used to dissect the adaptivemechanisms underlying ethanol tolerance in yeast. Six isogenicSaccharomyces cerevisiae populations of different ploidy (haploid,diploid and tetraploid) were asexually propagated in a turbidostat overa two-year period, with ethanol levels gradually increasing during theexperiment. This step-wise increase in exogenous ethanol levels resultedin a constant selective pressure for the cells. Whole-genome sequencingof evolved populations and isolated, ethanol-tolerant clones atdifferent times during the experiment allowed painting of a detailedpicture of the mutational dynamics in the different populations. Severalcommon themes in the type of adaptations and evolutionary mechanismsemerge, with all lines showing extensive clonal interference as well ascopy number variations. Additionally, the haploid and tetraploid linesshowed rapid convergence toward a diploid state. Despite these commonthemes, multiple lineage-specific adaptations were found, with littleoverlap in the mutated genes between the different populations. Byapplying a novel computational pipeline to identify affected pathways,overlapping between the functional modules affected in the differentadapted populations were revealed, with both novel and previouslyestablished pathways and genes contributing to ethanol tolerance.Importantly, introduction of specific mutated alleles present in adaptedpopulations into the ancestral strain significantly increased itsethanol tolerance, demonstrating the adaptive nature of these mutationsand the potential of using experimental evolution to unravel and improvea complex phenotype.

1.1 Evolved Cells are More Ethanol Tolerant

To study the mutational dynamics underlying increased ethanol tolerance,six prototrophic, isogenic S. cerevisiae strains of different ploidy(two haploid, two diploid and two tetraploid lines—with each line of thesame initial ploidy started from the same preculture (VK111, VK145 andVK202, respectively) were subjected to increasing levels of ethanol.This was done by gradually increasing ethanol levels from 6% (v/v) to12% over a two-year period in a continuous turbidostat with glucose (4%(w/v)) as a carbon source. Samples were taken at regular time intervalsand subjected to whole-genome sequencing analysis. In addition tosequencing each of the six evolving populations, the genomes of threeclones isolated from each of the population samples were also sequenced,resulting in a total of 34 population samples and 102 clonal samplesthat were sequenced. FIG. 1 depicts the experimental set-up as well asthe time points (number of generations) for which whole-genomesequencing was performed.

The relative fitness of the evolved populations was determined, isolatedat 40 and 200 generations, and generally observed increases in fitnessin high (9% v/v) EtOH (FIG. 2 and Materials and Methods). In general,clones taken from the same population at the same time point had similarfitness; with some notable exceptions suggesting considerableheterogeneity within populations (data not shown). These fitnessmeasurements were performed in 9% ethanol because the ancestralreference strains did not grow at higher ethanol levels. It is importantto note that the actual increase in fitness of the evolved strains inhigher ethanol levels (above 9% (v/v)) is much larger than what can beappreciated from FIG. 2. Spotting of the evolved lineages on agar plateswith increasing levels of ethanol indicates that these adapted strainscan grow on concentrations up to 11% (v/v) EtOH, with the controlancestral strains showing almost no growth under these conditions (i.e.,an infinite increase in fitness compared to the ancestral strain) (datanot shown).

1.2 Diploidization of Haploid Cells is a Fast Way of Increasing EthanolTolerance

Propidium iodide staining and flow cytometry analysis of evolvedpopulations showed that diploid cells appeared relatively quickly in theoriginally haploid populations (FIG. 3). This was observed independentlyin both reactors started from the same isogenic haploid population. Inboth reactors, diploid cells have taken over the entire population by130 generations. These diploid variants are still of mating type a, thesame as the initial haploid ancestral strain, indicating that they didnot arise through mating type switching and subsequent mating. Ourresults indicate that the diploids in our turbidostat are likely theresult of failed cytokinesis. The relatively rapid evolutionary sweep ofthe new diploid variants points to a significant selective advantage.Competition experiments confirmed that the fitness of a diploid cell isindeed significantly higher than that of an isogenic haploid strain in9% EtOH (p=0.0063; unpaired t-test), whereas there is no significantfitness difference in the absence of ethanol (p=0.469; unpaired t-test;data not shown). Interestingly, the tetraploid starting populations alsoconverged to a diploid state relatively fast during the evolutionexperiment. The parallel diploidization observed in two reactors withhaploid ancestral cells and two reactors with tetraploid ancestral cellsmight suggest that becoming diploid is one of the main and/or one of themore easily accessible routes leading to increased ethanol tolerance.

Whole-Genome Sequencing of Evolved Populations and Clones Reveals aComplex Pattern of Adaptation

To identify the mutational pathways during adaptation to ethanol,whole-genome sequencing of populations of evolving cells was performedthroughout the two-year experiment. Each population sample was sequencedto 500-fold coverage on average, and this for multiple time points (34samples for all reactors combined; see FIG. 1). In addition to thiswhole-population sequencing strategy, the genomes of three adaptedclones were also sequenced for each time point to 80-fold coverage onaverage, resulting in 102 clonal samples sequenced in total. Details onthe sequencing and analysis pipelines can be found in Materials andMethods.

Whole Genome Sequencing of Adapted Clones Reveals Mutators and ExtensiveAneuploidies

After 2 years, yielding around 200 generations, evolved clones(excluding clones from reactors 2 and 6, which acquired a mutatorphenotype, see below) contained, on average, 23 SNPs compared to theancestral strain (data not shown). This number is higher than what wouldbe expected based on measured rates of spontaneous mutations,^([50]) andcould reflect an increased mutation rate under the stressful conditionsimposed by the high ethanol levels in the set-up. Across all reactorsand clones sequenced, a total of 8932 different mutated sites wereidentified. The largest fraction are SNPs (6424 out of 8932; 72%);Indels are found mostly in non-coding regions (1830 out of 2508; 73%),whereas SNPs are mostly found inside genes (4971 out of 6424; 77%). Mostof these coding SNPs are non-synonymous (3672 out of 4971; 74%). In tworeactors (reactor 2 and reactor 6), a marked increase in the number ofmutations found in individual clones was noticed (see FIG. 4 and datanot shown). Specifically, clones from reactor 2 contain a significantlyhigher number of indels (about 929 on average per genome for clonesisolated after 200 generations, compared to 21 on average per genome forclones isolated after 200 generations in other reactors), whereas clonesfrom reactor 6 display an increase in the number of SNPs (about 1765 pergenome for clones isolated after 200 generations, compared to an averageof 31 per clone for other reactors). This high number of mutationspoints to a so-called “mutator” phenotype typically present in cellswith lower DNA replication fidelity and/or DNA repair. Interestingly,such mutators are frequently observed during evolution experiments,probably because mutators are more likely to acquire (combinations of)adaptive mutations faster.^([23, 51-54]) Interestingly, the increasedmutation rate in reactor 2 is first observed around generation 70-80,coinciding with the appearance of a 21 bp insertion in the MSH2 gene, akey player in DNA mismatch repair. Mutations in MSH2 have beenpreviously reported to confer a mutator phenotype,^([55, 56]) andmutations in MutS, the ortholog of MSH2 in E. coli, were identified in along-term evolution experiment and also result in a mutatorphenotype.^([23]) Indeed, deletion of this gene, as well as re-creatingthis insertion in an otherwise wild-type background, drasticallyincreases mutation rate (data not shown). The MSH2 mutation eventuallyreaches a frequency of 100% at the end of the evolution experiment (200generations). Apart from the convergence toward diploidy (see above),extensive copy number variation (CNVs) was also detected in the evolvedclones, comprising both duplicated and deleted chromosomal regions (seeFIG. 5 and data not shown). Other studies have observed similar copynumber variation during adaptation to stress, including heat stress andspecific nutrient limitations.^([24, 28, 33]) Interestingly, acquisitionof an extra copy of chromosome III appeared to be a common feature formost of the evolved clones (data not shown). Some (parts of) otherchromosomes, including chromosome XII and chromosome IV, are alsoduplicated in several of the evolved clones. These frequent occurrencesindicate that these specific aneuploidies could be adaptive underethanol conditions, although the exact mechanistic basis remains to beelucidated.

Whole-Genome Sequencing of Evolved Populations Reveals Extensive ClonalInterference

While the sequencing of clones yielded valuable information onindividual lineages within the evolving populations, sequencing ofevolving populations yielded more information on the complex mutationalpaths and dynamics between sub-populations within each evolvingpopulation. A distinct pattern of mutations appearing and disappearingover time in each of the six reactors was observed. Some of thesemutations remain in the population, eventually reaching high levels oreven complete fixation (i.e., presence in 100% of all cells in thepopulation). Other mutations only persist for a short time untillineages carrying these mutations are outcompeted by others, so called“clonal interference.” In total, 1637 mutations were identified acrossall populations and time points. 117 of these mutations are no longerpresent in the final time points sequenced, and 101 mutations drop morethan 10% in frequency after reaching their maximum frequency, indicativeof clonal interference. Interestingly, some overlap between themutations found in different independently evolving populations wasidentified (i.e., in different reactors). Specifically, it was foundthat 20 genes mutated twice in different generations and populations, 3genes mutated 3 times, 2 genes mutated twice, 2 genes mutated 5 times,and 1 gene mutated 6 times (data not shown). This significantly differsfrom what would be expected by chance (see Materials and Methods).Repeatedly hit genes are, amongst others, involved in stress response,cell cycle and heme biosynthesis. The higher number of sequenced samplesfrom reactors 1 and 2 allowed further analysis of these populationsequences and group mutations based on correlations in the changes intheir respective frequencies (based on the pipeline described inreference [32]; see also, Materials and Methods). This yields a moredetailed picture of the different co-evolving sub-populations present inthese reactors, which is depicted in the Muller diagrams of FIG. 6 (anddata not shown).

In both reactors, selective sweeps mostly consists of groups ofmutations that move through the population together. While thesereactors were inoculated with the same strain, the type and dynamics ofmutations observed during adaptation appear very different. However,both evolving populations of reactor 1 and reactor 2 are characterizedby strong clonal interference. In reactor 1, four differentsubpopulations are present around generation 90, each carrying differentmutations. By generation 130, these lineages have been outcompeted byanother lineage that almost completely dominates the population by 200generations (data not shown). In reactor 2, a lineage carrying amutation in PDE2 (encoding a high-affinity cAMP phosphodiesterase) isdriven to extinction by a subpopulation carrying indels in ASG1 andMSH2. ASG1 is a transcriptional regulator involved in the stressresponse and has been found mutated in evolved populations fromdifferent reactors (including reactor 1; see also, FIG. 6, and data notshown). Another example of clonal interference is observed in the latergenerations of this reactor: a subpopulation carrying a mutation inCDC27 (encoding a subunit of the anaphase promoting complex/cyclosome)is driven to extinction by a subpopulation carrying mutations in BNI1(important for nucleation of actin filaments) and PET123 (encoding amitochondrial ribosomal protein).

Diverse Pathways Involved in Adaptation to Ethanol

The results discussed above revealed extensive variability in the typeand number of mutations present in each evolving population. While thiscould suggest the presence of several, different mutational pathways(and thus lack of parallel evolution), mutations in different genesmight affect identical or similar pathways, implying that thephysiological adaptation to high ethanol might in fact be more similarthan what is immediately apparent from the individual mutations. To gaininsight into the affected biological pathways and investigate thepossible similarities in adaptation to increased ethanol, differentcomputational approaches were used. First, a term-enrichment analysiswas performed on the complete list of mutated genes for all reactors(for enriched clusters, see Table 1 and data not shown). Theseenrichment methods have been used as one of the standard functionalanalysis tools and gave a first insight into potential adaptive pathwayspresent in the evolved lineages.

TABLE 1 Top 10 functional clusters identified amongst genes hit bymutations in the evolution experiment. An enrichment score cutoff valueof 1.3, equivalent to a p-value of 0.05 for term enrichment wasused.^([57]) Cluster ranking Cluster name Enrichment score 1 ATP-binding3.54 2 Cellular bud 2.46 3 Leucine-rich repeat 2.23 4 Mating projection1.79 5 DNA damage response 1.76 6 Phosphorylation 1.63 7 ABCtransporters 1.60 8 Regulation of cell shape 1.47 9 tRNA aminoacylation1.44 10 Transcriptional regulation 1.36

In a second step, a sub-network-based selection method wasused^([58, 59]) (see Materials and Methods), first developed for E. coliexpression data. Here, this method was adapted and extended to selectthe subnetwork from the global yeast interaction network that bestconnected the mutated genes in the most parsimonious way. This methodalso identifies the intermediary genes involved in signaling mechanisms,which are not necessarily mutated in the evolved lineages but mediatethe cellular response. Mutations obtained from the populations with amutator phenotype were excluded (reactors 2 and 6) because of their lowsignal-to-noise ratio. This analysis identifies genes frequently mutatedin the different populations (DSK2, ASG1), as well as genes that areclosely connected on the interaction graph (HEM3, HEM12, . . . ). Thislatter set reflects parallelism at the pathway level in the differentreactors. From FIG. 7, it is also clear that sometimes multiplemutations occurring in the same pathway originated in the same reactorrather than across different reactors. This could indicate not only alevel of parallelism between the different reactors but also between thesub-populations in the same reactor. To confirm this, the sub-networkselection method was also separately applied on the mutations obtainedfor each of the populations. This confirms that within singlepopulations, the same pathways seem to be affected as those that areconsistently affected between the populations (including cell cycle, DNArepair and protoporphyrinogen metabolism, see also below). All resultingsub-networks as well as the enriched genes (including p-values) can befound in FIG. 7 (and data not shown). Additional analyses performed toincrease mechanistic insight, including interactome analyses of genescontaining non-synonymous mutations in the different population samples,as well as the effect of these mutations on protein functional domains,were also performed (data not shown).

Cell Cycle and DNA Replication

The analyses suggest that the cell cycle and DNA replication areaffected in the evolved lineages (FIG. 7). Ethanol has been previouslyreported to delay cell cycle progression, probably throughdepolarization of the actin cytoskeleton.^([60]) This could be relevantfor adaptation in the sense that slower growth could protect cells fromstress, perhaps by inducing genes involved in the general stressresponse.^([61-63])

Respiration

PheNetic analysis shows that protoporphyrinogen metabolism is affectedin the evolved populations (FIG. 7). Since this is important for hemebiosynthesis, this could indicate that respiration is affecteddownstream of mutations. Non-synonymous SNPs in functional domains ormodified sites are expected to have more effect on the cell thanmutations outside of these regions. It was found that such mutations inproteins involved in regulation and in enzymes of heme biosynthesis andproteins that need heme groups for proper functioning, are themselvesinvolved in respiration (data not shown). These analyses point to a rolefor changes in respiration in adaptation to ethanol. Together, theresults show evidence for parallel evolution, with the same processes(cell cycle and DNA replication) affected by different mutations, andalso highlight the plethora of processes involved in increased ethanoltolerance in the adapted lineages.

Example 2. Several High-Frequency Mutations Increase Ethanol Toleranceof Non-Evolved Strain

The high number of mutations (both SNPs and Indels) precluded anexhaustive analysis of all mutations present in the tolerant clones andpopulations and their effect on ethanol tolerance. One commonly usedapproach to investigate putative beneficial mutations is backcrossing ofevolved clones to their ancestor, which does not contain any mutation.However, since each of the evolved populations proved unable to form anyspores, this strategy was not accessible. Hence, the focus was on SNPsreaching high frequencies in the 200-generation population samples. Intotal, nine SNPs were selected for further study (Table 2). Several ofthe mutated genes belong to or are linked to the processes affectedacross and/or within specific reactors (heme metabolism, proteintransport and cell cycle, see also Table 2). These nine SNPs weresubsequently introduced into the ancestral haploid strain. The effect ofthese mutations was assessed by high-throughput competitionexperiments,^([64]) in 0, 4, 6 and 8% (v/v) EtOH conditions, withglucose as a carbon source (FIG. 8, data not shown). Several mutantsshow a clear increase in fitness, with the fitness effect oftendepending on the concentration of ethanol in the medium. Most mutantsshow slightly increased fitness in medium without added ethanol, withfurther increases in relative fitness with increasing exogenous ethanolconcentrations. Mutants in PRT1 and MEX67 are less fit than the WT innon-ethanol conditions, but show increased fitness in higher ethanollevels. MEX67 is a poly (A) RNA binding protein involved in nuclear mRNAexport. PRT1 encodes the eIF3b subunit of the eukaryotic translationinitiation factor 3 (eIF3). The increased ethanol tolerance associatedwith these mutations hints at translation processes as targets ofethanol. Interestingly, a recent study in E. coli showed that ethanolnegatively impacts transcription as well as translation.^([6]) However,if and how mutations in MEX67 and PRT1 might mitigate this effect andcontribute to ethanol tolerance remains unknown.

TABLE 2 SNPs in evolved lineages selected for introduction innon-ethanol- tolerant strain. SNPs reaching high frequencies in thedifferent evolved lineages were introduced in a haploid, non-ethanoltolerant strain. Gene functions were obtained from SGD, worldwideweb atyeastgenome.org. Nucleotide Function of mutated Enriched processLocation SNP type change gene (SGD) involved in Reactor 1 ChrIV: 1489310Intergenic A > T NA HST4 Non- G262C Histone deacetylase, Cell cycle*synonymous involved in cell cycle progression, SCFA metabolism VPS70Non- C595A Vacuolar protein Protein transport* synonymous sorting,unknown function Reactor 2 YBL059W Non- G479T Unknown function NAsynonymous PCA1 Non- C1583T P-type cation- Heme biosynthesis?*synonymous transporting ATPase (potential role in iron homeostasis)Reactor 3 ChrXII: 747403 Intergenic C > T NA HEM13 Non- G700C Hemebiosynthesis Protoporphyrinogen synonymous metabolism/heme biosynthesis*Reactor 5 PRT1 Non- A1384G eIF3 subunit, essential NA synonymous forprotein synthesis Reactor 6 MEX67 Non- G456A Component of nuclear NAsynonymous pore, mRNA export *Enriched process identified by PheNeticanalyses across all reactors

Of all mutations introduced into the ancestral strain, vps70C595Aprovided the largest fitness increase. VPS70 is putatively involved insorting of vacuolar carboxypeptidase Y to the vacuole.^([65]) A mutationin VPS70 has been recently identified by members of the team as adeterminant of ethanol tolerance in a Brazilian bioethanolstrain.^([66]) Interestingly, the VPS70 mutation in the evolvedethanol-tolerant lineages alters the same amino acid as the mutationpresent in the industrial ethanol-tolerant strain (A. Goovaerts and J.M. Thevelein, personal communication). The fact that one of themutations identified in the evolved lineages was also found in anindustrially used bio-ethanol strain underscores the potential of theapproach to find biologically relevant mutations for increased ethanoltolerance. Other members of the VPS family have been previouslyimplicated in ethanol tolerance as well, but also here, the exactmolecular mechanism through which they could increase ethanol toleranceis still unclear.^([10, 12, 67]) As far as is known, none of the othergenes investigated in this study have been previously implicated inethanol tolerance nor have these mutations been found in natural orindustrial yeasts so far. This implies that these mutations could beprime candidates to improve the ethanol tolerance and production ofexisting industrial yeasts.^([68])

Example 3. Testing of Mutations in Industrial Reference Strain

As a next step, the seven coding mutations shown in Table 2 (identifiedwith a lab yeast strain) were introduced into the industrial referencebiofuel strain Ethanol Red, a diploid Saccharomyces cerevisiae strain.For each of the coding mutations, the two wild-type alleles in EthanolRed were replaced with two mutant alleles (more details can be found inthe materials and methods section). Next, maximal ethanol accumulationin YP+35% (w/V) glucose was determined for each of these mutant strains.

The relative ethanol production of these mutants is summarized in FIG.9.

All but one of the mutants yielded higher ethanol titers. Statisticalsignificance was reached for the mex67 mutant. This mutation showed thehighest relative increase in the industrial strain. Of note, therelative increase in ethanol yield of around 4% is not 4% ABV extra, butan increase of 4% compared to the proper control. In practice, thiscorresponds to approximately 0.8% alcohol extra. All experiments wererepeated four times (real biological replicates, using independenttransformants where available).

Fermentation capacity in YP+35% glucose (w/v) of each of the mutants wascomparable to that of the wild-type Ethanol Red strain. Fermentationcapacity of these mutants is summarized in FIG. 10.

DISCUSSION

Many fundamental questions on the dynamics and genetics of adaptation toa complex and severe stress such as ethanol are still unanswered. Whichtype and number of mutations are needed and/or sufficient? To whatextent are these mutations and the pathways they affect predictable? Toaddress these questions, high-coverage whole-genome sequencing of clonesand populations was performed, isolated throughout a two-year evolutionexperiment and characterized their adaptation to increasing ethanollevels. This allowed painting a detailed picture of the mutationaldynamics in the evolving lineages.

The study demonstrates how many different evolutionary mechanisms allcome together to provide adaptation to a severe and complex stress.Specifically, it was found that adaptation to high ethanol levelsinvolves changes in ploidy, copy number variation and the appearance ofmutator phenotypes, with evolving populations showing strong clonalinterference. Although these mechanisms have been observed in otherstudies (see, for example, references 24, 27, 32, and 69-71), differentmechanisms are often observed and/or studied separately. Moreimportantly, in traditional evolution studies, populations are exposedto a fixed concentration of antibiotic or limiting nutrient. Under theseconditions, selection pressure is reduced or even eliminated when cellsbecome resistant. In the experimental set-up, ethanol levels werestepwise increased over time so that cells were evolving under constantselection. High concentrations of ethanol also actively killnon-tolerant cells, so that cells not adapted to the higher ethanolconcentrations die and/or are washed out of the turbidostat. Takentogether, the study combines aspects of traditional evolution studieswith principles used in morbidostat experiments.^([27]) Under suchsevere stress conditions (near-morbidostats), the number of generationsmay not be the ultimate way of measuring evolutionary time; cell deathand mutations that are not associated with DNA replication becomeincreasingly important, which could result in quick sweeps.

Extensive variation in genome size in the evolved cells was found, withaneuploidies in a large number of the evolved clones as well asconvergence toward a diploid state in the initial haploid and tetraploidpopulations. Becoming diploid thus appears to be a frequently usedstrategy by cells of different ploidy, which effectively increasesethanol tolerance. Convergence to diploidy has been observed in otherlaboratory evolution experiments.^([25, 36, 72]) In contrast to thesestudies, a clear fitness advantage of the diploid strains in ethanol wasdemonstrated. Although further research is needed to fully understandthis fitness advantage, changes in gene expression and/or cell sizecould be important factors contributing to the increased fitness ofdiploid cells.^([41, 73, 74]) Unfortunately, this convergent evolutionprevented a detailed analysis from being performed on the difference inadaptive strategies employed by haploids, diploids and tetraploids toincrease ethanol tolerance.

Aneuploidy and copy-number variation are increasingly recognized ascommon themes in rapid adaptation.^([24, 28, 69, 70]) It is believedthat changes in the copy number of chromosomes or chromosomal fragmentsprovide a relatively easily accessible way to change expression levelsof specific key genes,^([75, 76]) and these CNVs can provide large,usually condition-specific, fitness effects.^([77]) However, suchlarge-scale changes in the genome likely also have some unwanted,detrimental side effects, such as imbalance between gene products andgenome destabilization.^([78-80]) Evolving populations are believed togradually replace adaptive CNVs with more specific mutations that showfewer pleiotropic effects.^([28]) Notably, several of the evolvedclones, isolated from different reactors, carry an extra copy ofchromosome III and/or chromosome XII, pointing toward a potentialadaptive benefit of this specific aneuploidy. Interestingly, it wasfound that clones isolated at later time points have a specific, smallerregion of chromosome XII amplified (data not shown), indicating a morerefined solution. GO enrichment and network analyses of the repeatedlyamplified region of chromosome XII (position 657500-818000) hints atcell wall formation as one of the key processes affected by theseamplifications. Previous studies have indeed shown that cell wallstability is a key factor involved in ethanol tolerance.^([18])

Apart from diploidization of the evolving lineages, another example ofparallelism at the phenotypic level is the appearance of a mutatorphenotype in two of the six evolving populations. The sweep of the MSH2mutation in reactor 2 is likely caused by a so-called hitchhiking event,with the high mutation rates in the MSH2 mutant leading to theappearance of one or several beneficial mutations that drive theselective sweep. Because of the lower temporal resolution of sequencedpopulation samples of reactor 6, identifying the allele(s) underlyingthe mutator phenotype in this reactor has proven to be difficult.Parallelism at the genotypic level is less clear: few mutations andmutated genes shared between the different evolved lineages was found.Applying different types of network and enrichment analyses revealedfunctional modules affected in several of the adapted populations. Thesepathways include response to stress, intracellular signal transduction,cell cycle and pathways related to membrane composition and organization(such as isoprenoid metabolism, glycerophospholipid catabolism andfatty-acyl-coA metabolism). For some of these pathways, further work isneeded to clarify their exact involvement in ethanol tolerance.

To investigate the phenotypic effect of mutations present in the evolvedlineages, high-throughput fitness measurements in different ethanolconcentrations was performed. While the evolved lineages containedmultiple mutations, single mutations reaching high frequency in theevolved populations could already significantly increase ethanoltolerance when introduced into the ancestral, non-evolved haploidstrain. Moreover, several of these single mutations selected for furtherstudy also showed a (modest) fitness benefit in conditions with noethanol, with greater increases as ethanol levels rise. The number ofmutations identified in this study was too large to investigate thefitness effect of each individual mutation.

However, the strategy to select mutations that reached fixation in theevolved populations clearly proved successful, identifying as many asseven mutations (out of nine tested) that confer a fitness advantage inhigh ethanol environments. Mutations in genes linked to processesidentified as affected across the different reactors—protein transport(VPS70) and heme metabolism (HEM13 and PCA1)—indeed increased fitness inEtOH. This underscores the potential of PheNetic, a sub-network-basedselection method for identifying adaptive mutations. Two of themutations tested, ybl059wG479T and hst4G262C, did not increase fitness,although they reached high frequency in the adapted populations. This isindicative of hitch-hiking with other, beneficial mutations, or possibleepistatic interactions with other mutations.

Why then would not all feral yeasts show high ethanol tolerance, if itappears so easy to attain? First, it seems plausible that not all yeastsare confronted with selection for high ethanol tolerance. Furthermore,it is important to note that the fitness of the mutants under manydifferent conditions that mimic the natural habitats of yeasts has notbeen tested. It seems likely that some of the mutations identified inthis study would result in lower fitness in otherenvironments.^([81, 82]) Moreover, the effect of combined mutations hasnot been investigated. While it is possible that combining differentmutations could increase ethanol tolerance even further, it also seemslikely that some mutations and/or specific combinations of mutationscould lead to reduced fitness in different low and/or high ethanolenvironments. Indeed, while the clones have increased fitness in EtOH,fitness of several of the evolved clones (containing multiple othermutations apart from the ones investigated in this study) decreased inmedium without exogenous EtOH (data not shown). These results areindicative of antagonistic pleiotropy: the specific mutations present inthe evolved clones increase fitness in one condition (high ethanol,which was selected for), whereas they reduce fitness in otherenvironments. Ethanol resistance is an important trait for the survivalof feral yeasts in nature because the ethanol produced inhibits growthof competing microorganisms, while it serves as a carbon source in laterstages of growth, when all fermentable sugars are depleted (theso-called “make-accumulate-consume strategy”^([83-85])). The resultssuggest that adaptation to high ethanol is complex and can be reachedthrough different mutational pathways. Apart from yielding insight intothe evolutionary mechanisms leading to such complex and ecologicallyimportant phenotypes, the study is also of considerable industrialimportance. Several of the mutations identified in this study may beuseful to increase the ethanol tolerance of industrial strains used forthe production of alcoholic beverages or biofuels.

Materials and Methods

Strains Used in this Study

Starting strains for the evolution experiment are all derived from thehaploid prototrophic S288c strain FY5.^([86]) To prevent clumping ofcells during the evolution experiment, the flocculation genes FLO1,FLO10 and FLO11 were deleted in this strain using deletion cassettesbased on pUG6, conferring resistance to G-418 disulfate.^([87]) Markerswere removed through the Cre/LoxP technique using pSH65.^([88]) Matingtype switching of this strain was then performed, using plasmid pSB283,to create isogenic diploid and tetraploid strains. Fluorescent versionsof strains (YECitrine or mCherry tagged) were constructed by integratingfluorescent markers at an intergenic, neutral region of chromosome II.

Long-Term Selection

Populations were founded in 400 mL ethanol-containing media. Mediacontained 10 g/L yeast extract, 20 g/L bactopeptone, 4% (w/v) glucose,0.001% (v/v) Rhodorsil, Antifoam Silicone 46R, chloramphenicol (50μg/mL) and increasing concentrations of ethanol. Populations weremaintained at an average population size of 10¹⁰ cells. After 25generations, the level of EtOH in the media was increased each time(starting at 6% (v/v) and reaching 12% at 200 generations).

Turbidostat cultures were maintained using Sixfors reactors (INFORS®) at30° C., pH was kept constant at 5.0 with continuous mixing at 250 rpm inaerobic conditions. At regular times, a population sample was obtainedfrom each of the cultures for further analyses and stored in glycerol at−80° C. For DNA extraction purposes, a population cell pellet was alsofrozen down at −80° C.

Fitness Determination

Fitness for all evolved strains was determined in rich medium (YP, 2%(w/v) glucose) with 9% (v/v) ethanol, by competing strains against aYECitrine labeled ancestral strain. Cultures were pre-grown in YPD 6%ethanol. After 12 hours, wells of a 96-deep well plate were inoculatedwith equal numbers of labeled reference and unlabeled strains(approximately 10⁶ cells of each) and allowed to grow for around 10generations. Outer wells only contained medium and acted as a buffer toprevent ethanol evaporation. Additionally, plates were closed with anadhesive seal and plastic lid, and parafilm was used to prevent ethanolevaporation. Cultures were regularly transferred to new medium toprevent nutrient depletion. The ratio of the two competitors wasquantified at the initial and final time points by flow cytometry. Dataanalysis was done in FLowJo® version 10. Measurements were corrected forthe small percentage of labeled, non-fluorescent cells that occurredeven when the reference strain was cultured separately as well as forthe cost of YFP expression in the labeled reference strain. For eachfitness measurement, three independent replicates were performed. Theselective advantage, s, of each strain was calculated as s=(ln(Uf/Rf)−ln(Ui/Ri))/T where U and R are the numbers of unlabeled and referencestrain, respectively, the subscripts refer to final and initialpopulations and T is the number of generations that reference cells haveproliferated during the competition. The fitness of the unlabeled WTstrain was designated 1, fitness of the evolved strains as 1+s.

Determination of Cell Ploidy

DNA content of evolved populations and evolved clones was determined bystaining cells with propidium iodide (PI) and analyzing 50,000 cells byflow cytometry on a BD Influx. The ancestral haploid and diploid strainsused in the evolution experiment were used for calibration.

Whole Genome Sequencing

For evolved populations, genomic DNA was directly extracted from pelletsthat were frozen at the time of sample taking. Evolved clones wereselected from the different population samples by streaking glycerolstocks from the corresponding population samples on YPD plates. Swabsfrom each population were subsequently grown in YPD 6% EtOH anddilutions were plated on YPD plates with different ethanolconcentrations (ranging from 8% to 10%; with a 0.5% stepwise increase inethanol concentrations). From these plates, ethanol-tolerant clones wereselected and genomic DNA of these clones was extracted. Genomic DNA wasprepared using the QIAGEN® genomic tip kit. Final DNA concentrationswere measured using QUBIT®. Paired-end sequencing libraries with a meaninsert size of 500 bp were prepared and libraries were run on anILLUMINA® HISEQ® 2000 (EMBL GeneCore facility, Heidelberg). Averagesequencing coverage for clone and population sample is 80X and 500X,respectively.

Variant Calling, Filtration and Annotation

Adaptors removal and reads quality control were done by Trim Galore!(worldwideweb at bioinformatics.babraham.ac.uk/projects/trim_galore/)with options (-q 30-length 50). The clean reads were then mappedreference S. cerevisiae genome (S288C, version genebank64) using BurrowsWheeler Alignment allowing maximum 50 bp gaps.^([89]) To identifymutations, the BROAD Institute Genome Analysis Toolkit 628 (GATK,version 3.1) was used.^([90]) Performance of local realignment of readsaround Indels was begun, in order to eliminate false positives due tomisalignment of reads, which was followed by a base recalibration step.Then, SNP and Indel calling according to the GATK best practicerecommendations was performed. Population samples and single clonedatasets were analyzed independently. For population data, Indel callingwas performed using the UnifiedGenotyper tool, and the identifiedvariants were filtered based on the recommended criteria (QD<2.0,FS>200.0, ReadPosRankSum<−20 and InbreedingCoef<−0.8). For the SNPs, theUnifiedGenotyper was used to perform multi-sample SNP calling on all thepopulation samples together. The resulting multi-sample callset was thenused as a prior call to SNPs in all individual samples. The called SNPswere then subject to filtering (QD<2.0, FS>60, ReadPosRankSum<−8.0,MQ<40, MQRankSum<−12.5). Furthermore, the SNPs that were called insideregions containing Indels were filtered out. A list of all knownrepetitive regions and low complexity regions was generated using theprogram RepeatMasker (worldwideweb atrepeatmasker.org/cgibin/WEBRepeatMasker), which were masked out from theassemblies. Finally, variants present in the ancestral strain werefiltered out from all samples. A similar approach was used to analyzethe clonal samples. Ploidy level of each isolated clone was determinedby PI staining and variant calling was performed with UnifiedGenotyperfor haploid clones or HaplotypeCaller for diploid clones. Subsequently,SNPs and Indels were filtered based on the GATK best practicerecommendations as mentioned above. The inbreeding coefficient filterwas excluded in the clonal sample filtering for Indels, as it is apopulation-level metric. Annotation and effect prediction of allidentified variants was performed using snpEff.^([91]) Final processingof the data was performed using an R script, which involved filteringout variants called within the sub-telomeric regions (15 kbps from thechromosome ends) and, for population samples, variants whose frequencydid not change by more than 10% during the experiment. Theinfrastructure of the VSC—Flemish Supercomputer Center was used forthese analyses. For improved performance, CPU multithreading (-nct)capabilities of GATK were utilized, providing up to 20 cores and maximummemory capacity of 64 GB per process.

Identification of Copy Number Variations (CNV)

CNVs in the samples were identified using Nexus Copy Number software,version 7.5 (worldwideweb atbiodiscovery.com/software/nexus-copy-number). Reads were accumulated in500-base bins along all chromosomes, rejecting bins with less than tenreads. Log 2 ratios of copy numbers were then estimated from theread-depth counts in these intervals. The following calling parameterswere used: minimum number of probes per segment of 5, significancethreshold of p-value=1×10{circumflex over ( )}−9, percentage of removedoutliers of 5% and the limits for copy gain and loss of +0.25 and −0.25,respectively.

Statistical Analyses of Genes Hit Multiple Times

The p-values probabilities of genes being hit by a mutation a specificnumber of times were calculated using the binomial distribution. Thenumber of draws was set to the total number of mutations found insidecoding regions (i.e., 817); the number of successes was set to thenumber of times a specific gene was hit by a mutation (2 to 6); theprobability of success was set to the specific ratio of the length ofeach gene hit multiple times (in nucleotides) over the entire codingcontent of the S. cerevisiae genome (9080922 nucleotides).

Haplotype Reconstruction

Haplotype reconstruction was based on the approach described inreference [32]. Prior to the actual reconstruction, variants identifiedin the sequencing data were subject to further processing by excludingvariants that were multi-allelic, variants that exhibited mixed zygosityin the isolated clones (i.e., homozygous in one clone and heterozygousin another clone) and variants that did not reach the frequency of 0.2at any point during the experiment. Haplotypes (or “mutational cohorts”)present in the remaining variants were next reconstructed using aMATLAB® script (kindly provided by the Desai lab) on a local machinerunning MATLAB® 2013a. Briefly, variants present in the dataset wereclustered into haplotypes based on the Euclidean distance between theirfrequencies at specific time-points of the experiment. Afterward,frequencies of individual variants assigned to a specific haplotype wereaveraged to obtain the frequency of the haplotype itself. Frequencies ofthe identified haplotypes at specific time points (data not shown) werethen used as source data to draw their approximate Muller diagramrepresentations with INKSCAPE® 0.48.4.

Enrichment and Network Analysis

Functionally meaningful terms enriched in the list of genes hit in theevolution experiment were identified using DAVID Tools.^([57, 92])Overall, 170 clusters of functionally meaningful terms were identified,of which ten passed the statistical enrichment criteria. An enrichmentscore cutoff value of 1.3 was used, equivalent to a p-value of 0.05 forterm enrichment, as recommended in reference [57].

PheNetic Analyses

Intergenic mutations were discarded for the analysis. The interactionnetwork used as input for PheNetic was composed of interactomics dataobtained from KEGG^([93]) for metabolic interactions, String forprotein-protein interactions^([94]) and Yeastract for protein-DNAinteractions.^([95]) The total interaction network contains 6592 genesand 135266 interactions. This interaction network was converted to aprobabilistic network using the distribution of the out-degrees of theterminal nodes of the network edges. By doing so, edges connecting nodeswith a low out-degree will receive a high probability while edgesconnecting nodes with a high out-degree receive a low probability. Usinglists of mutated genes as input, PheNetic will now infer thatsub-network of the probabilistic network that best connects the mutatedgenes in the list over the probabilistic network. As the probabilisticnetwork penalizes hub nodes, the inferred sub-network will, therefore,preferentially connect the mutated genes through the least connectedparts of the network. This results in selecting the most specific partsof the network that can be associated with the mutated genes.

PheNetic was used to connect the mutated genes over the interactionnetwork^([58]) with the following parameters: 100-best paths with amaximum path length of 4 were sampled between the different mutations incombination with a search tree cutoff of 0.01. As the size of theselected sub-network by PheNetic is dependent on both a cost parameterand the number of mutated genes in the input, different costs were usedfor the sub-network inference from different sizes of mutated genelists. For the sub-network inference between all the mutated genes fromthe non-mutator reactors, a cost of 0.25 was used, for the non-mutatorreactors (1, 3, 4, 5), a cost of 0.05 was used as they all have asimilar amount of mutated genes, and for the mutator reactors (2 and 6),a cost of 0.5 was used.

Network Visualization and Enrichment

The resulting networks were visualized using CYTOSCAPE® and a functionalenrichment using the biological process terms of Gene Ontology incombination with the annotation of SGD of the sub-networks was performedusing the Bingo plugin, version 2.44.^([96])

Construction of Mutant Strains

Selected mutations identified from the whole-genome sequencing data wereintroduced into the ancestral genetic background using the followingprotocol. First, a selectable marker conferring resistance tohygromycine was introduced near the genomic location of interest throughhomologous recombination. Part of this locus was then amplified togetherwith the selectable marker using a forward primer containing the desiredmutation. The resulting PCR product was then transformed into theancestral strain; and presence of the mutation was verified by Sangersequencing.

High-Throughput Competitive Fitness Measurements

YECitrine- or mCherry-tagged site-directed mutant strains were competedwith the parental mCherry- or YECitrine-tagged strains, respectively, asdescribed in reference [64]. In brief, saturated cultures of mutant andparental strains were mixed in equivalent volumes and inoculated onto150 μl of YNB-low fluorescent medium in 96-well microtiter plates(CORNING® 3585). Micro-cultures grew without shaking and wereserial-diluted every 24 hours for approximately 28 generations (7 days)in a fully automated robotic system (TECAN® Freedom EVO200) thatintegrates a plate carrousel (LICONIC® STX110), a plate reader (TECAN®Infinite M1000), a 96-channel pipetting head, an orbital plate shaker,and a robotic manipulator arm. The equipment was maintained in anenvironmental room at constant temperature (30° C.) and relativehumidity (70%). Fluorescence signal (mCherry: Ex 587 nm/5 nm and Em 610nm/5 nm; YECitrine: Ex 514 nm/5 nm and Em 529 nm/5 nm) and absorbance at600 nm were monitored every hour during the entire experiment. TheYECitrine- or mCherry-tagged parental strains were competed to eachother for normalization and monitored individually to determinebackground fluorescence signal. Fluorescence and absorbance output datawas analyzed in MATLAB® as described in reference [64] to obtain anaverage selection coefficient, smut, with its S.E.M. from threeexperimental replicates.

Introduction of Selected Alleles in Ethanol Red

Selected mutations identified from the whole-genome sequencing data wereintroduced into the diploid strain Ethanol Red using the followingprotocol. First, a selectable marker conferring resistance to hygromycinwas introduced near the genomic location of interest through homologousrecombination. Part of this locus was then amplified together with theselectable marker using a primer containing the desired mutation. Theresulting PCR product was then transformed into Ethanol Red; andpresence of the mutation was first identified by PCR using 3′ mismatchprimer pairs, and subsequently verified by Sanger sequencing. Second, aselectable marker conferring resistance to nourseothricin was introducedinto Ethanol Red near the genomic location of interest. Part of thislocus with the desired mutation was amplified together with the marker.The PCR product was then transformed into the corresponding mutants,which already has one copy of the target gene mutated and linked tohygromycin resistance marker. The selection of successful transformantswas performed on double selection, and the mutants were first identifiedby PCR, then verified by Sanger sequencing.

Protocol Very-High Gravity (VHG) Fermentations

Lab-scale fermentations under VHG conditions were started with anovernight pre-growth of a single colony into 3 ml YPD, followed bytransfer of the entire culture to 30 ml YP+4% (w/v) glucose andadditional growth for 48 hours to the stationary phase (200 rpm, 30°C.); 250-ml Schott bottles each filled with 150 ml YP+35% (w/v) glucoseand a magnetic rod (35×5 mm) were inoculated to a starting OD₆₀₀=1.0(approximately 2.0×10⁷ cells/ml). These bottles were sealed with awaterlock and stirred continuously at 150 rpm on a magnetic stirringplatform (IKA® RO 15) at 30° C. The bottles were weighed daily todetermine the cumulative weight loss, a proxy for CO₂ production andfermentations were stopped after 7 days. The ethanol production wasdetermined using Anton Paar Alcolyzer. A Tukey HSD test was performed tocheck for significant differences in ethanol production between mutantsand their respective controls.

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The invention claimed is:
 1. A genetically modified Saccharomyces spp.comprising a MEX67 allele having a G to A mutation at a positionrelative to position 456 of SEQ ID NO:
 1. 2. The Saccharomyces spp. ofclaim 1 further comprising one or more additional alcohol tolerancealleles and/or accumulation modulating alleles.
 3. The Saccharomycesspp. of claim 2, wherein the one or more additional alcohol tolerancealleles and/or accumulation modulating alleles are selected from thegroup consisting of PCA1, PRT1, YBL059W, HEM13, HST4, and VPS70.
 4. TheSaccharomyces spp. of claim 2, wherein the additional alcohol toleranceallele(s) and/or accumulation modulating allele(s) are selected from thegroup consisting of PRT1 with a A to G mutation at a position relativeto position 1384 of SEQ ID NO: 3, YBL059W with a G to T mutation at aposition relative to position 479 of SEQ ID NO: 4, HEM13 with a G to Cmutation at a position relative to position 700 of SEQ ID NO: 5, HST4with a G to C mutation at a position relative to position 262 of SEQ IDNO: 6, and VPS70 with a C to A mutation at a position relative toposition 595 of SEQ ID NO:
 7. 5. The Saccharomyces spp. of claim 1,wherein the Saccharomyces spp. is Saccharomyces cerevisiae.
 6. TheSaccharomyces spp. of claim 1, wherein the MEX67 allele is SEQ ID NO: 1with a G456A mutation.